mlsanityData to 7 Oct 2026

Prediction of Scene Plausibility

arXiv 2212.01470 ↗First posted 2 Dec 2022cs.CVcited by 0

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Abstract (arXiv)

Understanding the 3D world from 2D images involves more than detection and segmentation of the objects within the scene. It also includes the interpretation of the structure and arrangement of the scene elements. Such understanding is often rooted in recognizing the physical world and its limitations, and in prior knowledge as to how similar typical scenes are arranged. In this research we pose a new challenge for neural network (or other) scene understanding algorithms - can they distinguish between plausible and implausible scenes? Plausibility can be defined both in terms of physical properties and in terms of functional and typical arrangements. Hence, we define plausibility as the probability of encountering a given scene in the real physical world. We build a dataset of synthetic images containing both plausible and implausible scenes, and test the success of various vision models in the task of recognizing and understanding plausibility.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 33 references that are in the map. First posted on arXiv 2 Dec 2022.

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21 of 33 references.

References · 21

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9 more
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